Spatiotemporal empirical mode decomposition of resting-state fMRI signals: application to global signal regression

Abstract

Resting-state functional connectivity MRI (rs-fcMRI) is a common method for mapping functional brain networks. However, estimation of these networks is affected by the presence of a common global systemic noise, or global signal (GS). Previous studies have shown that the common preprocessing steps of removing the GS may create spurious correlations between brain regions. In this paper, we decompose fMRI signals into 5 spatial and 3 temporal intrinsic mode functions (SIMF and TIMF, respectively) by means of the empirical mode decomposition (EMD), which is an adaptive data-driven method widely used to analyze nonlinear and nonstationary phenomena. For each SIMF, brain connectivity matrices were computed by means of the Pearson correlation between TIMFs of different brain areas. Thus, instead of a single connectivity matrix, we obtained 5 × 3 = 15 functional connectivity matrices. Given the high value obtained for large-scale topological measures such as transitivity, in the low spatial maps (SIMF3, SIMF4, and SIMF5), our results suggest that these maps can be considered as spatial global signal masks. Thus, the spatiotemporal EMD of fMRI signals automatically regressed out the GS, although, interestingly, the removed noisy component was voxel-specific. We compared the performance of our method with the conventional GS regression and to the results when the GS was not removed. While the correlation pattern identified by the other methods suffers from a low level of precision, our approach demonstrated a high level of accuracy in extracting the correct correlation between different brain regions.

Robust smoothing of one‐dimensional data with missing and/or outlier values

“…Noise-assisted versions called ensemble EMD (EEMD) [12], complementary EEMD (CEEMD) [13] and the CEEMD with adaptive noise (CEEMDAN) [14] have been developed to further improve the decomposition capabilities of the EMD approach. The EMD-based decomposition techniques have been utilized in wide range of applications, especially in the area of signal denoising/smoothing [11][12][13][14][15][16][17]. In the EMD-based decomposition techniques, the IMFs are extracted in the descending order of their frequency contents.…”

“…However, the computation cost of EEMD-based approaches is much higher than that of the basic EMD. In addition, residual noise could be introduced in the IMFs as a side effect of the utilized noise-assisted technique [16].…”